Building an almanac my family could use during a game gave me a chance to see how far I could take a project with an AI agent. Now that it worked, I wanted to look back at my own involvement: what I needed to understand, what I could leave to the agent, and how we found ways to work together.
I Didn’t Need To Know Every Detail
You’ll notice that throughout this whole series, I haven’t mentioned the specific technologies used for data extraction, hosting the local website, etc. The code was Python (57 separate programs for extraction and reformatting files!), the website is powered via TypeScript, HTML, CSS, the database is SQLite, and review artifacts are stored in JSON, Markdown, and TSV. However, those are details that I never even needed to think about, and only know about due to curiosity when I was watching it work, and just now looking at GitHub as I wrote this.
For production code or something I needed to support longer term, I think more familiarity with the code and architecture is warranted – but for smaller tools such as this, it is possible to be completely removed from the specifics of technologies used and implementation, and via interacting with the agent, be concerned only with the outcomes and end goals.
Reflecting On The Project As A Whole
I began this project because an agent changed the cost of trying. I could give it an uncertain direction, let it spend hours exploring, and decide whether the result was worth another step. The failed approaches did not consume the same amount of my own time, so an idea that would normally have required a more expensive commitment to learning how to decode old DAT files became reasonable to pursue.
What changed after that was not simply the amount of work the agent could do. We gradually found better ways to divide the work between us.
When chat and folders became tedious, we created pages I could review from a phone or tablet. When an image format resisted ordinary inspection, I supplied screenshots from the running game and looked for a recognizable signal among the agent’s experiments. When a polished rewrite lost details, we modeled completeness as a relationship in a database that my agent could both document and later validate. When real gameplay found missing searches, the agent expanded each example into a broader audit.
The Distinction Between Judgment, Decisions and Implementation
Each time, my judgment began as a small observation: this page is hard to review, those arches look familiar, this paragraph lost too much, this phrase should have been found. Collaborating with the agent and delegating (with varying levels of specificity, and sometimes varying degrees of success) could turn that observation into a tool or a repeatable check, then apply it beyond the example I had supplied.
That was the type of collaboration I was excited to have: compared to coding in a high-level language where I’m still specifying the logic myself, here I was directing broadly and delegating implementation decisions. The agent could usually fill in gaps independently from directions given much less precisely than actual code, and then run the experiments. Meanwhile, I provided guidance on whether the process was producing the right kind of answer and clarified the goals when it needed redirecting. I was generally successful in this, as you saw in Part 4 where I gave broad directions and the agent solved the implementation details for the maps by itself, while also learning in Part 5 that I still needed to identify any requirements that I absolutely needed the agent to adhere to (in that case, lossless paraphrasing).
To finally bring the project home, we then went in the opposite direction. After using the agent to explore broadly, I had to narrow the final product back to the original need. Pruning carefully let the core of the product become the entire experience, and helped the almanac disappear and feel like an intuitive part of a broader experience.
We started with an old geography game, a pile of unfamiliar files, and an idea I previously would have been skeptical about investing in, given both the manual work involved and the uncertainty around effort and success. We ended with an almanac my family could open on a tablet, use to crack clues during a case and perhaps even learn a thing or two in the process.